An image may be represented in a memory as an array of stored pixels. Techniques for image sharpening are well known. The sharpness of the image is a measure of the high frequency content of a line scanned video signal representing a video scan line through the image. Sharper images have generally larger changes in intensity from one pixel to another, and therefore tend to have higher frequency content. Conversely, less sharp or blurry images have generally smaller changes in intensity from one pixel to another, and therefore tend to have less higher frequency content.
As is known to those skilled in the art, an image may be sharpened by creating a blurred (unsharp) image from the original image, and then subtracting the unsharp image from the original image to create a difference image. The difference image is then scaled (multiplied) by a sharpening factor, SF, a selected parameter of the sharpening process. The original image is combined with the scaled difference image to form a sharpened. image. Thus, EQU final=original+SF*(original-unsharp)
The problem in the prior art is that the two factors which control sharpening, i.e. the sharpening factor SF, and the amount of blurring are typically adjusted by an operator. More blurring will result in a more pronounced sharpening effect. Less blurring works better on images which are already somewhat sharp and therefore need less sharpening.
A more subtle problem exists in some images where the sharpness of the image varies within the same photograph. Uneven sharpness can result from motion blur where a moving object (or a moving background) has a difference in sharpness. Also, photographs taken with limited depth of field have areas with different sharpness. In some photographs the effect of finite depth of field is very pronounced, but it exists to some extent in almost every photograph. Too much sharpening produces a grainy effect in some areas (such as gray sky), while not enough sharpening leaves some areas (such as faces) blurry.